在联贝叶斯矢量自回归中子空间收缩
1University of Salzburg Salzburg Austria.
概括
这项研究引入了一种新的贝叶斯矢量自回归 (VAR) 与子空间收缩前,有效地合并VAR和因子模型. 该方法准确地确定了许多因素,并改善了宏观经济预测.
科学领域:
- 计量经济学 计量经济学
- 宏观经济的建模.
- 贝叶斯统计学 贝叶斯统计学
背景情况:
- 经济学家经常选择大向量自回归 (VAR) 和因子模型来分析广泛的数据集.
- 在应用于大规模宏观经济数据时,VAR和因子模型都有局限性.
研究的目的:
- 开发一个统一的贝叶斯矢量自回归 (VAR) 框架,整合因子模型的优势.
- 引入一个子空间收缩前值,允许同时估计因素数量和收缩强度.
主要方法:
- 开发一个结合贝叶斯VAR模型,其中包含一个子空间收缩.
- 对前者属性的理论分析.
- 模拟研究以评估因子数检测和预测性能.
主要成果:
- 拟议的子空间收缩前成功地确定了模拟中的因素数量.
- 贝叶斯式VAR与子空间收缩前显示了对美国宏观经济数据的预测准确度的提高.
- 该模型允许灵活估计因子模型子空间和收缩强度.
结论:
- 带有子空间收缩前值的联合贝叶斯VAR为大规模宏观经济分析提供了一种强大而灵活的方法.
- 这种综合方法提高了与传统独立模型相比的预测性能.
- 该方法提供了一个原则性的方法来结合来自VAR和因子模型的信息.
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